孟德智 王鈺煒 王明軍 秦啟波 俞暉



摘 要: 針對快速時變信道,提出了一種基于復指數基擴展模型(CE-BEM)的線性最小均方誤差(LMMSE)信道估計方法,對信道估計的結果使用離散長橢球序列(DPSS)進行平滑處理,并相應提出了基于迭代的載波間干擾(ICI)消除信道均衡方法.仿真結果表明:在高多普勒信道場景下,該方法系統誤碼性能較傳統信道估計方法有一定程度的提升.
關鍵詞: 信道估計; 基擴展模型; 快速時變信道; 載波間干擾(ICI)消除
中圖分類號: TN 929.5文獻標志碼: A文章編號: 1000-5137(2019)01-0064-06
Abstract: In the fast time-varying channel scenario,a linear minimum mean square error (LMMSE) channel estimation method based on complex exponential basis extension model (CE-BEM) was proposed,and the channel estimation results were smoothed by use of discrete prolate spheroidal sequence (DPSS).Meanwhile,a channel equalization method based on iterative inter-carrier interference (ICI) cancellation was proposed.The simulation results showed that the proposed method had a better system error performance than traditional estimation method in the high Doppler channel scenario.
Key words: channel estimation; base extended model; fast time-varying channel; inter-carrier interference (ICI) cancellation
0 引 言
高速移動環境中,無線信道表現出頻率及時間的選擇性衰落,傳統的信道模型不能很好地對高速時變的信道進行模擬,由此,基擴展模型(BEM)[1-2]得到廣泛的應用.
文獻[3-5]中,作者提出了基于導頻的BEM信道估計方法.MA等[3]時域中插入導頻,解決符號間干擾(ISI)的問題.KANNU等[4]在時域中插入導頻,解決載波間干擾(ICI)的問題.STAMOULIS等[5]作者在文獻[4]的基礎上,研究得出ICI大多發生在相鄰的子載波,導致信道矩陣可近似地被認為是帶狀的,并相應地給出了信道均衡的迭代算法,降低了運算復雜度.
本文作者基于復指數(CE)[1]函數,提出了一種在頻域中插入導頻簇的信道估計方法,并采用離散長橢球序列(DPSS)[6]對信道估計出的結果進行平滑處理.仿真結果表明:在高多普勒的信道場景下,附加DPSS平滑處理的信道估計方法優于復指數基擴展模型(CE-BEM).
1 系統和信道模型
1.1 OFDM系統模型
4 結 論
本文作者提出了采用DPSS平滑處理的CE-BEM信道估計方法,相應給出了基于迭代的ICI消除的信道均衡算法,并在高速移動信道下進行仿真實驗.結果表明:相比傳統LS和MMSE算法,以及CE-BEM方法,帶有PPSS平滑處理的CE-BEM的信道估計方法具備更為精確的估計精度.
參考文獻:
[1] TANG Z,CANNIZZARO R C,LEUS G,et al.Pilot-assisted time-varying channel estimation for OFDM systems [J].IEEE Transactions on Signal Processing,2007,55(5):2226-2238.
[2] TANG Z,LEUS G,BANELLI P.Pilot-assisted time-varying OFDM channel estimation based on multiple OFDM symbols [C]//Signal Processing Advances in Wireless Communications.Canne:IEEE,2006:1-5.
[3] MA X,GIANNAKIS G B,OHNO S.Optimal training for block transmissions over doubly selective wireless fading channels [J].IEEE Transactions on Signal Processing,2003,51(5):1351-1366.
[4] KANNU A R,SCHNITER P.MSE-optimal training for linear time-varying channels [C]//IEEE International Conference on Acoustics Speech and Signal Processing.Philadelphia:IEEE,2005:789-792.
[5] STAMOULIS A,DIGGAVI S N,AL-DHAHIR N.Intercarrier interference in MIMO OFDM [J].IEEE Transactions on Signal Processing,2002,50(10):2451-2464.
[6] CHENG P,CHEN Z,RUI Y,et al.Channel estimation for OFDM systems over doubly selective channels:a distributed compressive sensing based approach [J].IEEE Transactions on Communications,2013,61(10):4173-4185.
[7] 任大孟.快速時變信道下無線 OFDM 系統信道估計技術的研究 [D].哈爾濱:哈爾濱工程大學,2009.
REN D M.Research on channel estimation techniques of wireless OFDM systems in fast time-varying channel [D].Harbin:Harbin Engineering University,2009.
[8] ZEMEN T,MECKLENBRAUKER C F.Time-variant channel estimation using discrete prolate spheroidal sequences [J].IEEE Transactions on Signal Processing,2005,53(9):3597-3607.
[9] International Telecommunication Union.Guidelines for evaluation of radio transmission technologies for IMT-2000 [R].Geneva:ITU,1997.
(責任編輯:馮珍珍,包震宇)